Publisher DOI: 10.1007/s11071-021-06786-5
Title: bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems
Language: English
Authors: Stender, Merten  
Hoffmann, Norbert  
Keywords: Basin of attraction;Bi-stability;Clustering;Monte Carlo simulation;Multi-stability;Nonlinear dynamics
Issue Date: 2021
Source: Nonlinear Dynamics : (2021) (in press; CC BY 4.0)
Journal: Nonlinear dynamics 
Abstract (english): 
The pervasiveness of multi-stability in nonlinear dynamical systems calls for novel concepts of stability and a consistent quantification of long-term behavior. The basin stability is a global stability metric that builds on estimating the basin of attraction volumes by Monte Carlo sampling. The computation involves extensive numerical time integrations, attractor characterization, and clustering of trajectories. We introduce bSTAB, an open-source software project that aims at enabling researchers to efficiently compute the basin stability of their dynamical systems with minimal efforts and in a highly automated manner. The source code, available at https://github.com/TUHH-DYN/bSTAB/, is available for the programming language Matlab featuring parallelization for distributed computing, automated sensitivity and bifurcation analysis as well as plotting functionalities. We illustrate the versatility and robustness of bSTAB for four canonical dynamical systems from several fields of nonlinear dynamics featuring periodic and chaotic dynamics, complicated multi-stability, non-smooth dynamics, and fractal basins of attraction. The bSTAB projects aims at fostering interdisciplinary scientific collaborations in the field of nonlinear dynamics and is driven by the interaction and contribution of the community to the software package.
URI: http://hdl.handle.net/11420/11067
ISSN: 1573-269X
Institute: Strukturdynamik M-14 
Document Type: Article
Project: Wege zur Steigerung der Energiedissipation und Dämpfung in selbsterregten Strukturen mit irregulären Schwingungsantworten - Kombination datenbasierter Verfahren mit modellbasierten Zugängen 
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